JOURNAL ARTICLE

iRoach: A Design and Implementation of Mobile Robot with Integrated LIDAR, GPS and IoT Sensors for Environmental Sensing.

  • Published In: Grenze International Journal of Engineering & Technology (GIJET), 2026, v. 12, n. Part2. P. 3827 1 of 3

  • Database: Applied Science & Technology Source Ultimate 2 of 3

  • Authored By: Anand; Pujari, Veeresh 3 of 3

Abstract

This paper presents iRoach, an autonomous mobile robot designed for robust and advanced environmental monitoring. Built around the reliable Arduino Uno platform, iRoach integrates a diverse suite of sensors, including temperature, humidity, light, gas, motion (IMU), LIDAR, camera, and GPS, all powered by a long-lasting lithium battery. The system leverages servo and gear motors for agile locomotion and employs LIDAR for real-time obstacle detection and 3D mapping. Seamless wireless connectivity with a dedicated mobile app enables remote monitoring, manual control, and live visualization of environmental data. The implementation of a buzzer and alert system further ensures immediate hazard notification. iRoach demonstrates effective navigation in complex, cluttered, or hazardous environments, making it highly suitable for applications such as search and rescue, industrial safety checks, and environmental research. The results highlight its versatility, scalability, and the significant impact of bio-inspired, multi-sensor robotic platforms in advancing real-time environmental assessment. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Grenze International Journal of Engineering & Technology (GIJET). 2026/01, Vol. 12, Issue Part2, p3827
  • Document Type:Article
  • Subject Area:Geography and Cartography
  • Publication Date:2026
  • ISSN:23955287
  • Accession Number:192273115
  • Copyright Statement:Copyright of Grenze International Journal of Engineering & Technology (GIJET) is the property of GRENZE Scientific Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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